Tagged: BAbip

I’m trying to figure out this BABIP (batting average on balls in play) puzzle. Orioles first baseman Chris Davis, who notched a BABIP south of .324 just once in his career before last year (.275 in 2010), saw said statistic drop by almost 100 points in 2014. It’s easy to point to the defensive shift as a cause — when defenses shift on you 83 percent of the time, you almost have to — but I’m reluctant to buy in on this just yet.

Unfortunately, there is not much, if any that I know of, publicly-available defensive shift data. Prior to the 2014 season, Jeff Zimmerman published 2013 data courtesy of The 2014 Bill James Almanac. A haphazard calculation yields a 2013 “shift BABIP” about 18 points, or about 6 percent, lower than the MLB aggregate BABIP of .297. During the 2014 season, an ESPN feature projected defensive shifts to reach an all-time high, and by quite a margin, too. In light of this, one could hypothesize that more overall shifts would cause a lower aggregate BABIP. However, MLB’s aggregate BABIP in 2014 was .298.

None of this really tells us a whole lot. The shift BABIP would be awesome if it could be broken down by location of the ball in play — accordingly, I would strictly focus on a pull-side shift BABIP — but, alas, it does not. FanGraphs also breaks down a hitter’s spray chart numerically — you can view Davis’ pull-side splits here — but it does not indicate how many times defenses shifted against him when he pulled the ball. Until this gap in the data can be both a) plugged and b) made publicly available, the answers we seek regarding the true effectiveness of the shift may evade us.

No matter, because I still want to try to figure some things out. Let’s talk a little bit of theory. Like a hitter’s BABIP, I think his shift BABIP is also likely to be volatile. No matter where you place your fielders, you cannot predict where a batter will hit the ball. If you study the spray charts and play the probabilities just right, you’ll surely turn a few more would-be hits into outs. But just like regular BABIP, there will still be an element of luck involved.

… I see all sorts of luck. I think the mistake is made when one relates shift percentage with shift BABIP. I expect more shifts to correlate with greater effectiveness — results that would be reflected in the hitter’s depressed batting average. But more shifts does not equate to greater effectiveness on a per-play basis, which is essentially what shift BABIP measures. In short: given that a player’s batted ball profile is identical year to year, his shift BABIP should have some semblance of consistency. We know that BABIP is pretty volatile, but there is a small element of consistency to it (for example, Edwin Encarnacion‘s BABIP is perennially stuck in the mid-.200s while Mike Trout‘s is typically buoyed in the upper-.300s). Thus, I would expect shift BABIP to exhibit at least a little bit of consistency, and for that consistency to produce consistently lower marks than that of the regular BABIP.

Yet Davis’ pull-side BABIP dropped from .338 to .185. The decrease makes sense intuitively, but he saw the fewest shifts in 2012 and actually had a worse pull-side BABIP than he did in 2013. I don’t have to run a regression to show there’s no correlation to be found there (albeit in a minuscule sample size). Now, his increasing tendency to pull the ball (43.3% in 2012, 46.2% in 2013, 50.9% in 2014): that is something that should correlate well with shift BABIP. Because the shift BABIP doesn’t differentiate among ball placement, where the player hits the ball ought to affect his shift BABIP, especially if he predominantly pulls the ball. Thus, an increase in balls in play to the pull side should correlate with a decrease in shift BABIP. Despite all this, Davis recorded his highest shift BABIP during the year he pulled the ball with the least amount of authority.

Now, forgive me, but I have to try to make something of all of this. Let’s take the 6-percent decrease in aggregate BABIP when accounting for shifts (from earlier), and let’s say that teams shift on Davis 100 percent of the time. (It’s not unfathomable, given defenses shifted against him five times out of six, and it appears — it appears — to have succeeded with flying colors.) Given an identical batted ball profile from year to year, maybe I could expect his BABIP, which sat at .335 and .336 the two years prior to 2014, to fall to around .315 permanently. Even if his “true” BABIP benchmark is closer to .300, then maybe his overall shift BABIP is in the .280 ballpark. As he hits more and more balls to his pull side, his shift BABIP will decrease, as will his batting average. That I can fathom.

But I cannot bring myself to accept that a 10-percent increase in pull-side balls-in-play from 2013 to 2014 correlates with a 24-percent decrease in shift BABIP. I don’t think the latter can reasonably be larger than the former without a significant luck element involved. Then again, the 7-percent increase in pull-side balls from 2012 to 2013 resulted in a 17-percent decrease in shift BABIP produces an almost identical ratio (24/10 = 2.4, 17/7 = 2.429), so maybe there’s something I’m missing. But allow me to speak hypothetically: Let’s say Davis puts 100 balls in play, consisting of 50 to his pull side and 50 everywhere else. This silly 2.4-to-1 ratio demonstrates that one more ball hit to the pull side — that is, now he hits 51 balls to the pull side and 49 everywhere else — means not only is that one extra pulled ball an automatic out but also almost one-and-a-half more balls not to the pull side become outs. It’s simply incomprehensible, and I maintain that a percentage increase in balls hit to the pull side would correlate with at most a percentage decrease in shift BABIP.

Wrapping things up: I think it goes without saying that Davis got unlucky in the BABIP department in 2014 — it’s more a matter of determining how unlucky and why. I think his shift BABIPs betray Davis; I think he got especially lucky against the shift in 2012 and especially unlucky in 2014. In general, more shifts should suppress a hitter’s batting average but not his shift BABIP, and it’s Davis’ shift and pull-side BABIPs that absolutely tanked in 2014. Considering he still managed to hit a home run in 5 percent of his plate appearances, I a full 600 from Davis to yield at least 30 bombs, and I think that’s a modest projection. Couple that with a batting average rebound — which I fully expect at this point, strikeout rate disclaimers withstanding — and the down-and-out Davis could be a nice draft day bargain.

Analysts toss around terms such as BAbip without explaining how to interpret them or why they’re significant. Similarly, the websites that provide the statistics, such as Baseball Reference and FanGraphs, define the metrics but do little to deconstruct them for readers. This is a tutorial for anyone who is not familiar with advanced metrics and wants to learn more about them.

BAbip, or batting average on balls in play, is a metric that quantifies how often a ball put into play by a batter turns into a hit. Because it concerns only balls in play, it excludes home runs and walks and includes sacrifice flies.

A player’s BAbip for a single season can be understood by comparing it to his career rate, or what I have referred to as the “norm.” These comparisons can help you predict if a player is over-performing or under-performing, the key word being “predict.” BAbip that significantly deviates from the norm does not guarantee it will regress toward the norm; a deviation across a large sample size (in this context, an entire season) is much less likely to happen but is not impossible.

Some basics: The benchmark BAbip is around .300, although each player creates his own norm. Speedy guys tend to have higher BAbips (Mike Trout‘s is .370) because they can leg out ground balls. Power guys tend to have lower BAbips (Edwin Encarnacion‘s is .275). Although a player exerts some influence, BAbip is largely a function of the defense handling the balls the player puts into play. Some ground balls escape the gloves of clunkier infielders; some line drives find the gloves of roving outfielders. So it is important to note that a player’s BAbip involves some random deviation (luck). Take a look the Arizona Diamondbacks’ Martin Prado‘s splits circa the 2013 season:

The abbreviated table above was generated near the end of the 2013 season. Prado, a career .292 hitter, struggled through the first half of the season, hitting only .253 with a .668 OPS. His BAbip was a lowly .260 at the time, much lower than his career .311 mark. That’s a large deviation; if I were a fantasy owner looking to capitalize, I would bank on Prado bouncing back. As the table shows, Prado paid dividends to the owners who stuck with him (or the ones who capitalized via trade), batting .322 with a .321 BAbip with 13 games to go in the season. The second-half BAbip is high, but combining it with his first-half mark produces a .284 BAbip — not quite the norm, but much closer than how he performed before the All-Star Break.

Davis had hit 50 home runs with 13 games to play, with 37 of coming in the first half of the season (you can view his 2013 splits here). This is relevant because Davis’ HR/FB rate before the All-Star Break was, if I’m not mistaken, around 28 percent, significantly higher than his current mark of 22.8 percent. Twenty-eight percent was awfully high, even for Davis; a savvy statistician (aka fantasy baseball nerd) would have expected his home run rate to regress toward the norm, around 16 percent.

Because Davis didn’t break out until 2012, his career HR/FB may be a bit deflated. But even the large difference between 2013 and his career rate indicates Davis is a candidate to regress in 2014. Had his HR/FB rate in 2013 been closer to something like 18 percent, Davis would have been closer to 40 home runs than 50.

In short, compare a player’s HR/FB to his career mark, which is what is normal for him, to try to determine whether he has been getting lucky (or unlucky, or neither) on home runs. Strong deviation from the norm is a likely predictor of regression, for better or for worse. HR/FB is not the end-all, be-all to explaining a player’s performance, but it can greatly benefit the owner willing to exploit its predictive attributes.

In this scenario, “everyone” is probably right. But I’m part of the camp where each member believes it when he sees it. (I don’t know who runs this camp or where the counselors are, but it’s fun and I like it.)

I would love to buy into the Taijuan Walker craze right now, especially with so many talented young rookies making an impact this year and last. But Walker’s Triple-A line is a point of concern for me. Sure, he’s sitting on a 3.61 ERA and an attractive 10.0 K/9. But the WHIP… yikes, the WHIP and that BB/9 rate, man. A WHIP of 1.413 and coupled with more than four walks per nine innings scares me. It makes me think he wasn’t ready to transition to Triple-A, let alone the big show.

In his defense, his WHIP suffers from an inflated .331 BAbip. After adjusting it to his career rate, his WHIP is closer to 1.3 rather than 1.4, and once you consider he’s pitching in the allegedly hitter-friendly Pacific Coast League, it would drop under 1.3, which makes it ultimately serviceable. But the walks are a problem for me. The 10 strikeouts-per-nine are flashy, but hitters in the majors are simply better — they will walk more and strike out less, and I think Walker’s slowly-increasing walk rate could spell problems for the young future ace in his first stint in the majors.

I’m not saying he won’t develop into an ace. Even New York Mets phenom Matt Harvey posted a 3.9 BB/9 and 1.32 WHIP during his stint in Triple-A before his call-up. I’m just saying maybe temper your expectations a bit. Don’t be surprised if there’s a prolonged adjustment period — for Pete’s sake, the kid’s 20 years old. And if you’re expecting him to change the landscape of the rest of your fantasy season, odds are he won’t.

But with the way these darned kids are impacting the game these days — and the way Safeco Field plays like a pitcher’s park — I like Walker as a nice sleeper in 2014. Too bad he’s got too much hype to be a sleeper. Maybe if you’re really keen on him, you’ll hope his debut is underwhelming.

Anyway, despite everything I just wrote, I would start pretty much anyone against the Astros these days. So if you’re thinking about streaming Walker tomorrow — my first streamer suggestion of the year! — go for it.

Jack Moore of CBSSports.com expressed concern July 17 about Seattle Mariners shortstop Brad Miller striking out too much. At the time, he was batting only .246 with an understandably disconcerting 23.5 percent strikeout rate, and Moore was concerned Miller was overmatched.

Perhaps Moore’s words were prophetic (in a reverse psychology kind of way), or perhaps the All-Star Break did Miller a bit of good. Since the Break, Miller’s strikeout rate has been only 11.4 percent, less than half of what it had been up until the Break and well below the MLB average. His strikeout rate now splits the difference at 16.2 percent — exactly the same as his minor league rate. Meanwhile, his walk rate declined only slightly, from 10.3 to 8.6 percent.

Although Miller has only batted .271 over the same span (raising his season average to .261), his low strikeout rate means he has been putting a ton of balls in play, and his BAbip (batting average on balls in play) is a lowly .275.

Miller’s minor league BAbip? .388. That’s right — .388.

It sounds crazy, and I understand if you are quick to dismiss Miller’s more-than-impressive minor league batting average of .334, but hear me out. His .388 BAbip (and .334 average) is the result of 999 minor league plate appearances — equivalent to, what, a season and a half-worth of MLB games? Not exactly what you’d call a small sample size. Also, players do post crazy-high BAbips. Well, one player does. His name is Mike Trout, and he posted a .383 BAbip in 2012 and a .371 BAbip so far in 2013. I can guarantee you Miller is not the next Mike Trout, but still, it can be done.

At a fundamental level, a sub-.300 BAbip is the norm for power hitters such as Blue Jays slugger Edwin Encarnacion, not speedy guys like Miller. If Miller boasts a .330 BAbip for the rest of 2013 and even the rest of his career, I wouldn’t be the least bit surprised — and his batting average would greatly benefit from such a massive boost in average on balls in play.

A middle infielder who has 15-homer, 15-steal, .300/.400/.500-slash line potential? Sign me up. I’m in love.

Now, for my favorite part: Brad “The Triple Machine” Miller has five triples in a mere 165 at-bats. Take a look at which hitters have hit triples most frequently this year, in terms of at-bats per triple:

Brad Miller, 33.0

Freddy Galvis, 37.5

Munenori Kawasaki, 45.5

Carlos Gomez, 46.6

Starling Marte, 51.4

Stephen Drew, 52.5

Will Venable, 54.5

Mike Trout, 57.1

Jean Segura, 58.1

Jacoby Ellsbury, 60.3

Just look at the names on that list. Like I said, “The Triple Machine” is fast — even if he isn’t the most proficient base stealer — and I would hesitate to dismiss his lofty BAbip so quickly.

And I don’t care if “The Triple Machine” doesn’t roll off the tongue. It’s the best nickname. EVER! You can thank me later, Brad.

New York Yankees pitcher CC Sabathia was pummeled for a fourth straight game Friday, giving up five earned on 11 hits. He has now allowed 27 runs (22 earned) in his last four starts spanning 19 2/3 innings, pushing his ERA up to a whopping 4.78.

That’s horrifying.

What’s wrong with him? Before coming to any conclusions, let’s look at a variety of metrics and measurements.

His FIP is 4.19 and his xFIP 3.61, significantly lower than his ERA. That’s comforting.

His BAbip is slightly inflated, at .315.

His HR/9 rate has ballooned to 1.41, the first time in his career it is greater than one, and his HR/FB rate is at 14.9 percent (according to FanGraphs), almost 6 percent higher than his career mark. Pair this information with a slightly depressed LOB% and it makes sense why he keeps giving up runs.

His K/9 and BB/9 rates are down, but they’re nothing abnormal, as both are better than the marks he put up in 2009 or 2010 when he was in the running for AL Cy Young. Again, comforting.

His fastball is down about 1.5 mph… According to FanGraphs’ pitch values, it’s arguably the second-worst fastball in Major League Baseball at -14.7 runs above average, behind only the truly awful Joe Blanton. Uh oh.

However, it looks like his fastball was pretty bad last year (at -11.9 runs above average) but still had an admirable year. His slider was much better last year, though.

He’s throwing first-pitch strikes at the second-highest rate of his career.

Batter contact rates are down inside the zone, but up outside the zone. Weird.

His run support has dropped by almost two full runs per game. That doesn’t help his win total, but it also doesn’t affect his ERA.

So… What does it all mean? I don’t know. Honestly. Sabathia’s xFIP indicates he’s running into some bad luck, as does his BAbip, HR/9 and HR/FB rates. But there’s a lot of talk about his declining velocity and his fastball becoming more hittable. If a pitcher is easier to hit, more balls will be put into play and BAbip may increase naturally as the batter is better able to square up the ball. However, none of his other peripherals really indicate anything is wrong. The percentage of pitches inducing contact, swinging strikes, swings in the zone and swings outside the zone fluctuate yearly, and his 2013 numbers are not abnormal. His WHIP is high, but it’s elevated somewhat by his inability to go as deep into games (down from about 7.1 IP per game in 2012 to about 6.6 IP in 2013) and the lofty BAbip.

Ultimately, if your league’s trade deadline hasn’t yet come and gone , I still wouldn’t look at Sabathia as a buy-low candidate. Even if his ratios regress to something more tolerable, his lack of run support will render any improvements meaningless. However, I don’t see why Sabathia shouldn’t bounce back next year. I don’t know if I would rank him as highly as he was in 2013, but I doubt many experts will anyway. And unless his fastball has suddenly failed him a la Tim Lincecum, I see Sabathia returning to form..

One last note: Sabathia’s “pace,” or average time between pitches as measured by FanGraphs, is 22.6, the lowest of his career. Maybe all he needs to do is slow down his game.